Several possible solutions have been proposed to solve issues related to FLchain performances. The research in [1]} proposes a new efficient communication protocol that is able to compress uplink and downlink communications while remaining high robustness to the increased number of clients, and data distribution. These properties can be achieved by using a combination of sparsification, ternarization, error accumulation, and optimal Golomb encoding techniques for uplink compression and speeding up parallel training in the global server without compromising learning convergence. Moreover, a new optimization algorithm is proposed in [2]} for FL-based IoT networks, called FetchSGD, that can train high-quality models for communication efficiency. At each communication round, clients compute a gradient based on their local data, then compress the gradient using a data structure called a Count Sketch before sending it to the central aggregator. The aggregator maintains momentum and error accumulation count sketches, and the weight update applied at each round is extracted from the error accumulation sketch. In this way, the proposed scheme reduces the amount of communication required each round, while still conforming to the training quality requirements of the federated setting. Further, designing lightweight consensus mechanisms to mitigate block mining delays is desired to boost the overall FLchain training speed, which can be done by implementing lightweight block validation schemes [3]}. The most important data such as local client updates can be prioritized to add to the block for the consensus among clients in the FLchain update aggregation, while signature assignment and block verification can be simplified with respect to the reputation of each client. For example, the client with higher reputation due to frequent update training contribution can be given a higher priority in the verification stage for fast block approval, which helps mitigate the delay and bias in the model aggregation and in turn improves the overall FLchain training performances. Further, to solve the heterogeneous issues in the FL training, the work in [4]} proposes a robust FL framework in heterogeneous environments where device clients may behave abnormally due to its behaviours and data points at different devices have different distributions. A modular algorithm is designed with three steps, including computing empirical risk minimizers, computing outlier-robust clustering algorithm, and implementing a distributed optimization on each cluster, aiming to solve the high dimension issues from heterogeneous behaviours of different devices in each network group.
